Various entities involved in the distribution of television programs are keenly interested in determining the number of viewers who have watched a particular program. For instance, among many applications, these entities may use this information to adjust the schedule of programs, the lineup of channels, the assessed value of advertisements that air during certain programs, and so forth.
The television industry relies on a variety of statistical measurements to reflect the number of viewers who have watched a particular program. Two common measurements are ratings and shares. A rating measurement is representative of the number of television units that have presented a particular program relative to a total number of television units that were capable of presenting this program. For instance, assume that a television network provides services to a hypothetical pool of 100 set-top boxes associated with 100 television units. If 30 of these set-top boxes are tuned to a particular program, then the rating of that program is 30%. On the other hand, a share measurement is representative of the number of television units that presented a particular program relative to a total number of television units that were actually presenting programs in a prescribed time frame. For example, assume that 30 set-top boxes in the above example were tuned to a particular program, but only 60 set-top boxes of the entire pool of 100 set-top were turned on. In this case, the share of this program is 50%.
The industry has provided a number of techniques that can be used to collect the raw data from which rating and share measurements can be computed. In one traditional technique, an entity conducting a survey selects a sample pool of viewers and forwards viewing logs (also known as diaries) to these viewers. The entity instructs these viewers to record an indication of the programs that they have watched over a prescribed period of time, and then, at the end of this time, to send the logs back to the entity. The entity then aggregates the entries in the logs and computes various statistics.
Because of the complex nature of viewing habits, entities conducting surveys may make various simplifying assumptions regarding a viewer's behavior. For instance, it is common to instruct a viewer to indicate that the viewer has watched a program if the viewer has watched the program for more than a prescribed amount of time (e.g., 8 minutes).
There is room for considerable improvement to traditional techniques for computing viewing statistics. For instance, the above-described manual technique of computing statistics is labor-intensive and error-prone. Further, the simplifying assumptions used in traditional techniques have the potential of providing skewed—that is, potentially inaccurate—viewing statistics. More specifically, the simplifying assumptions can sometimes result in viewing estimates that are unduly high, and/or viewing estimates that are potentially contradictory (e.g., by falsely indicating that a user simultaneously watched two programs within a given reporting interval).
There is accordingly a need for more convenient and reliable techniques for generating statistics that reflect the consumption of media resources, such as, but not limited to, television programs.